About me

Learning and Inference from Complex, Imperfect, and Uncertain Data

I am a fourth-year Ph.D. candidate in the Department of Computer Science at Western University, advised by Prof. Grace Y. Yi and Prof. Boyu Wang.

My research broadly spans statistics and machine learning. I am interested in developing principled methodologies for learning, inference, and decision-making from complex, imperfect, and uncertain data. My work combines ideas from statistical modeling, machine learning, uncertainty quantification, and robust learning.

Broadly, I am interested in probabilistic and latent variable modeling, missing data, causal inference, trustworthy AI, foundation models, and other emerging areas of data-driven discovery. I am particularly interested in applications to healthcare and biomedical research, including multimodal electronic health records, medical imaging, and large language models.

Research Interests

Statistical Machine Learning Learning with Noisy and Imperfect Data Reliable and Uncertainty-Aware AI Bayesian Deep Learning Biomedical AI

Working Papers

Instance-Dependent Bayesian Modeling and Robust Training with Crowdsourced Noisy Data (in preparation)

Hui Guo, Grace Y. Yi (2026)
Reliability-Aware Longitudinal Clinical Prediction from Multimodal Electronic Health Records (in preparation)

Hui Guo, Yi Wan, Boyu Wang, Grace Y. Yi (2026)

Selected Publications

Dual-Granularity Learning for Regression with Continuous Noisy Labels

Hui Guo, Boyu Wang, Grace Y. Yi

Published in Neural Information Processing Systems (NeurIPS), 2026
Addressing both Variable Selection and Misclassified Responses with Parametric and Semiparametric Methods

Hui Guo, Grace Y. Yi, Boyu Wang

Published in Bernoulli 32(2): 1303–1327, 2026

Paper Code arXiv
Revisiting Source-Free Domain Adaptation: a New Perspective via Uncertainty Control

Gezheng Xu*, Hui Guo*, Li Yi, Charles Ling, Boyu Wang, Grace Y. Yi

Published in International Conference on Learning Representations (ICLR), 2025

* Equal contribution

Paper Code Poster
Physician Effects in Critical Care: A Causal Inference Approach Through Propensity Weighting with Parametric and Super Learning Methods

Yuan Bian, Yu Shi, Hui Guo, Grace Y. Yi, Wenqing He

Published in Journal of Data Science 23(1): 130–148, 2025

Paper Code Poster
Learning from Noisy Labels via Conditional Distributionally Robust Optimization

Hui Guo, Grace Y. Yi, Boyu Wang

Published in Neural Information Processing Systems (NeurIPS), 2024

Paper Code arXiv Poster
Label Correction of Crowdsourced Noisy Annotations with an Instance-Dependent Noise Transition Model

Hui Guo, Boyu Wang, Grace Y. Yi

Published in Neural Information Processing Systems (NeurIPS), 2023

Paper Code Poster

Selected Talks

Learning with Noisy Labels: Variable Selection and Misclassification Probability Modeling

Lightning talk for Dr. Raymond Carroll's visit, London, Canada, June 2025

Slides
Case Study: Personalized Physician Recommendation for Critical Care Using the TreeSHAP Method

Annual Meeting of the Statistical Society of Canada, Online, May 2022

Poster